text:
\section{Introduction}
A job scheduler or task queue system has largely been deployed in the modern distributed computing architectures to ensure the execution and delivery of tasks for billion-scaled volume process requests, including Airbnb Dynein \cite{schedulingairbnb}, Meta FOQS \cite{schedulingfacebook}, Google Cloud Task Queue \cite{schedulingoogle}, AWS Job Queue EC2 \cite{schedulingaws}. The advantages of the scheduled tasks on the asynchronous computing architecture include the decoupling from user request, the reduced request latency, the better scalability and management with the rate-limiting, retry, and priority control \cite{schedulingoogle}. The scheduled jobs are particularly suitable for various high complex applications with affordable tolerate delays, such as graph processing, notifications, video encoding service, and language translation. Those scheduled applications can be personalized and executed at per user basis by providing the user information with its execution timestamps in the task queue via the job scheduler. In social network, it is important to deliver or update information to users at the right time to maintain the user experience and the execution impact. However, no mature personalized execution time optimization solutions for the scheduled jobs have been reported in the industrial applications.

Notifications are important recommendation channels in social network products to deliver information to users and the delivery time has been demonstrated to be an important factor in the delivery process \cite{pejovic2014interruptme} \cite{ho2018nurture}. In the real-world, a user receives over 65 notifications on average per day and almost 50\% of notifications have been reported to interrupt the users' daily work tasks \cite{pielot2014situ}. A wide range of factors has been reported to contribute to the user engagement with notifications, including the device context and status \cite{okoshi2017attention}, real-time environment, and the content relevance of the notifications \cite{pielot2014situ}. Different from the notifications directly triggered by user activities (\textit{e.g.}, direct messages), the notifications for promotional purposes (\textit{e.g.}, suggesting new friends to connect) are generally scheduled in advance \cite{liu2020reinforcement}. Due to the nature of the scheduled job, the device-level or environment signals cannot be directly used to improve the delivery time of promotional notifications.

A successful execution time optimization service not only protects the user experience from interruption by notifications but also improves the execution efficiency by avoiding overwhelming infrastructure resource consumption. The modern recommender system faces a dilemma in how to maintain the timely update and quick response to user actions while consume reasonable infrastructure resource, especially under the exponential growth of massive candidate pools \cite{khanal2020systematic}. The periodic candidate generation approach has been widely used in various billion-scale web applications, including LinkedIn \cite{agarwal2015personalizing}, YouTube \cite{covington2016deep}, and Amazon \cite{linden2003amazon}. Due to the infrastructure resource constraint, it is challenge to update the candidate generation to users at the right time to reflect the up-to-date information, especially for the large group of non-daily active users. A reinforcement learning approach has been introduced to optimize the personalized number of execution time in the scheduled job to balance daily metrics and resource consumption \cite{liu2020reinforcement}, and the following question is how to define the personalized execution timestamp(s) after the number of execution time is determined in the job scheduler.

In this paper, we frame the execution time optimization as a learning-to-rank problem to select the best execution time slots for the scheduled jobs. We encounter four major challenges in the execution time optimization service development process. First, as opposed to the traditional learning-to-rank problems, the best time selection problem demonstrates strong dependency and interactions among each ranking objects, \textit{i.e.} the selection of one time slot will largely affect the utility function performance on the other time slots, similar to the problems in slate ranking \cite{bello2018seq2slate}. Second, the single-channel utility function may not fit each use case, especially on the channel-inactive users. We then propose a signal assembly approach to ensemble the user temporal activity signals cross different channels in the best time decision making. Third, the number of execution time varies a lot in different product use cases (from once per week to 24 daily), which makes it infeasible to implement a generic conditional probability-based approach. We propose a simple yet robust technique termed ``best time policy'' to solve the best time ranking problem. Last, we observed cannibalization cross use cases to compete the user's peak time slot and impair the global user experience. We propose a tiered coordination system to mitigate the best time cannibalization issue. We make the following novel contributions in this study:
\begin{itemize}
    \item Formulate the execution time optimization problem as a learning-to-rank problem and propose to solve the best time selection for the scheduled jobs with a ``best time policy''.
    \item Propose a ensemble learning approach to minimize the ranking loss by leveraging different user temporal activity signals in the best time decision making process.
    \item Observe the cannibalization effect cross different use cases in the best time selection.
    \item Evaluate our method with production traffic that serves billions of users per day, and demonstrate positive gains in daily metrics in several applications.
\end{itemize}

To our best knowledge, our work has been the first ML-based algorithm to support the multi-tenant application to solve the execution time optimization problem for the scheduled jobs at a large industrial scale cross different product domains.

\section{Related work}
\label{related_work}
The works relevant to this study come from large-scale server-site social media or delivery platform. Multiple email marketing services have been developed for clients to schedule email campaign and distribute emails at the optimal time per day \cite{kalantari2021trackers}, including MailChimp and Selligent. Pinterest reported a budget pacer to allocate weekly notification budge cross days of the week to each user and then scheduled the notification to be delivered at the user's peak time of the day \cite{zhao2018notification}. However, those optimization solutions can only provide limited peak time slots (\textit{e.g.} once) per day and thus cannot satisfy the business needs to daily schedule a diverse range of the number of execution time. In addition, the delivery/update of various types of notifications or candidate generation at the same peak time slot raises the cannibalization risk cross different use cases to impair the user experience. Several studies have been published on the volume or channel selection optimization of LinkedIn notifications \cite{gao2018near} and a logistic regression model was proposed to make a send/drop decision for each generated email \cite{gupta2017optimizing}. However, those studies focus on the delivery phase filtering instead of the execution time optimization in the scheduling-phase. In the candidate computation applications, YouTube reported a batch-oriented precomputation approach to calculate the video recommendations to minimize the serving latency and it updates data sets several times per day to mitigate the delay between the recommendation generation and the recommendation serving \cite{davidson2010youtube}. LinkedIn reported a fast OLAP serving system Avatara to provide rich structured data insights to users, which is updated multiple times per day \cite{wu2012avatara}. Unfortunately, no execution timing strategy have been reported in these enterprise applications.

The learning-to-rank has been widely applied in the information retrieval and other recommender systems \cite{chapelle2011future}. Most of those applications implicitly assume independence in the ranking object relevance and return the top sorted candidates to the users. The diversity issue or the popularity bias has been proposed in the long-tail recommendation to impact the recommendation quality \cite{yan2021diversification}. In addition, a conditional probability distribution approach has been proposed to represent the relation information among ranking objects in the global ranking \cite{qin2008global}. One of the similarities of these learning-to-rank applications is that the ranking decisions result in ``one-off'' impact on the users and the viewer generally view the ranked results (e.g. search ranking) as a whole. In contrast, the best time ranking problem releases its effect on users in a time-series fashion with strong interaction among ranking objects.

There is relevant branch of research on the notification timing optimization using various heuristic rules  \cite{fischer2011investigating}, supervised learning \cite{pejovic2014interruptme} or reinforcement learning approach \cite{ho2018nurture}. However, these studies focus on the real-time mobile device-site optimization with various device and context signals, and thus they are largely different from our server-site applications which are scheduled in advance (\textit{e.g.} one day ahead of delivery).

\section{Methodology}
\label{methodology}
To start, we formalize the execution time optimization problem for the scheduled jobs. Given our formulation, we present a high-performing centralized approach that can scale to hundreds of billions of decisions made by hundreds of product use cases per day.
\subsection{Problem overview}
Given a job that has needs to be executed within a time range, the task of timing optimization for the scheduled jobs is to identify the best \textit{N} time slots. The best time slots are those that i) optimize product-specific downstream metrics, and ii) minimize infrastructure load to avoid overwhelming the services in the execution phase. For example, when we schedule the promotional notifications, we need to determine the execution timestamps for each user in the task queue so that the notifications are delivered to the users at the right time. The time slots are defined based on the server-site timezone to be consistent with the server-site logging. In practice, the execution time range and the number of time slots defined in the scheduling request vary significantly (\textit{e.g.} from once per week to up to 24 times daily) among different product use cases. In addition, the number of time slots in the scheduling request can be fixed or personalized value in the same use case \cite{liu2020reinforcement}. To provide a centralized multi-tenant scheduled jobs optimizer, our solution needs to be highly transparent, stable, and customizable to accommodate significant variation in the execution time optimization problem.

\subsection{Time Slots Ranking}
The optimizer takes as input a scheduling request $r$, which contains the product use case $p$ and the user $u$. To enable our optimizer to handle multiple different problem settings simultaneously, each request also includes the execution time range [$t_{start}$, $t_{end}$], the number $N_{u,p}$ of time slots to return, and the length $I$ of each time slot (\textit{e.g.}, one hour). Given $I$, we assume the execution time range is partitioned into a set of contiguous candidate time slots $\mathcal{T}$ = \{$t_1$, $t_2$, ..., $t_k$\} $(k \ge N)$, though the proposed methodologies can be applied to a more general space (\textit{e.g.} infrastructure off-peak hours) with minor modifications.

The request also specifies the prediction metric $m$ to optimize for, and a best time policy $\mathcal{\pi}$, which is a pre-defined policy that acts on a time slot ranking to generate a list of execution time as output. This output, $\mathcal{T}_e$, is returned to the job scheduler and stored in the task queue. To prevent a spike in the infra load during the execution, we generally apply a random delay $\epsilon \in [0, I)$ to the execution time in the job scheduler, which results in a uniform distribution of the actual execution timestamps within each time slot.

To generate the time slot ranking, we create a user temporal activity map $V_{u,m}$, that maps the time slot $t$ to its personalized predicted value $\widetilde{m_{u, t}}$. This value is generated by a learned utility function $f_m(u,t)$ to represent the user's activity on $t$. These predicted values can be used to directly rank time slots, or multiple relevant values can be combined via a signal assembler to generate a time slot ranking.

\subsection{Features}
The features we use to represent the user's activity on each time slot is important to the performance of the utility predictor $f_m(u,t)$. We define the feature vector $F(u,t)$ for each user - execution time slot pair as follows:
\begin{itemize}
    \item User features: This feature class includes the user's profile features (country, age, etc), and the user's activity level (daily active user or not, etc)
    \item Local time features: This feature class transforms the server-site time slot to the user's local time (day of the week, hour of the day, sunrise and sunset time, holiday or not, etc)
    \item User activity features: This hourly resolution feature class represents the major ranking signals in the best time ranking problem. The user's historical activity data are processed to generate the user's activity features for the user $u$ at the time slot $t$ of the day $d$ within a series of tracking window $k$ (\textit{e.g.} the range of $[t - 4 \; hours, t]$ and $[t, t + 4 \; hours]$). In addition, the 7-day signals are then aggregated to generate the weekly user hourly activity signals.
\end{itemize}

\subsection{Utility Functions}
We provide a series of utility functions $f_m$ to represent the user's activity $m_{u,t}$ in each time slot on various offsite (\textit{e.g.} push, email) and in-app channels. These utility functions fall into the following three categories:

\begin{itemize}
    \item pClick models: Classification models that predict the likelihood of a user $u$ clicking the notifications if delivered on the time slot $t$.
    \item Activity models: Regression models that predict the user $u$'s activity metric $m$ on the time slot $t$.
    \item Activity counters: Real-time stream processing service to store the user $u$'s activity counter on the time slot $t$.
\end{itemize}

These scores are normalized to [0.0, 1.0] by taking
\begin{equation}
  \widetilde{m_{u,t}} = \frac{m_{u, t} - \min(m)}{\max(m) - \min(m)}
\end{equation}
where $\max(m)$ and $\min(m)$ are the maximal and minimal prediction value of metric $m$ across all users and time slots, respectively.

\subsection{Best Time Signal Assembly}
As a multi-tenant optimization service, it is infeasible to provide a perfect utility function for each use case given the limited infrastructure resources. In addition, the model quality of the product-specific utility functions cannot be well maintained due to the sparsity of positive labelling. Accordingly, it is important to leverage the off-the-shelf users activity signals to support different product optimization requests. Moreover, by leveraging the users activity signals across different channels, we can mitigate the impact of user inactivity on certain delivery channel, which is a common issue in the notifications \cite{gao2018near}.

We adopted a strategy to provide a variety of generic utility functions to cover the user's activity on different delivery channels. These signals are combined using a product-specific signal assembler to generate a personalized utility metric $M^{p}_{u,t}$ for each product use case ${p}$. To make it well explainable and debuggable, we define the signal assembler as the weighted sum of various user activity signals $\widetilde{m_{u,t}}$:
\begin{equation}
    M_{u,t,p} = \sum_{m \in M} { \omega_{m,p} \times \mathcal{A}_{u, m} \times \widetilde{m_{u,t}}}
\end{equation}

where $\omega_{m,p}$ are the product-specific metric weights and $\mathcal{A}_{u, m}$ is the user's activity on the offsite or in-app channel associated with $m$. The optimal weights $\omega_{m,p}$ can be tuned based on the online experiments or via an offline ensemble learning approach as follows. Given a product use case ${p}$ and its ground-truth orders of the user's sorted active time slots ranking lists $\mathcal{T}_{u,p}$, we define the ensemble ranking loss function as the average of the squared differences between the actual rank index and the weighted sum of the predicted rank index from various user activity signals for each user's active time slots. We observed that weights learned from this loss function is more aligned with online experiment results than those from NDCG as loss function.
\begin{equation}
    Loss_p = \frac{1}{n} \times \sum_{u} \sum_{t \in \mathcal{T}_{u,p}} (\hat{rank_t} - rank_{t,p})^2
\end{equation}
where $rank_{u,t,p}$ is the index of $t$ in $\mathcal{T}_{u,p}$ and $\hat{rank_t} = \linebreak\sum_{m \in M} { \omega_{m,p} \times \widetilde{\mathcal{A}_{u, m} \times \hat{rank_{t,m}}}}$. We then used the feature-weighted linear stacking \cite{sill2009feature} to learn the optimal
$\omega_{m,p} = \arg \min (Loss_p)$.

\subsection{Best Time Policy}
We observed that the execution decision on one time slot may strongly affect the outcomes of executing on others, and as a result the top N ranking approach generally does not result in optimal performance (Table ~\ref{besthourpolicy-table}). We propose an explainable yet robust technique called ``Best Time Policy'' on top of the utility functions. These policies return the best time slots based on the provided temporal activity map and the number of best time slots. An example of the best time selection using the avoid nearby policy is shown in Figure~\ref{fig:besthourpolicy} and its implementation is documented in the Algorithm~\ref{algorithm:avoidnearby}. Our optimizer can be further extended to support more use case-specific best time policy based on the product context.

\begin{figure}
\centering
\begin{minipage}{1.0\linewidth}
  \includegraphics[width=\linewidth]{figures/Figure1.png}
  \captionof{figure}{An example of applying avoid 1-hour nearby best time policy to return three execution timestamps. After the current peak hour is selected, the peak hour and its nearby hours are removed from the temporal activity map $V_{u,m}$, from where the next peak hour is selected.}
  \label{fig:besthourpolicy}
\end{minipage}
\end{figure}

\begin{algorithm}
	\caption{Avoid $w$-slot nearby best time policy}
	\begin{algorithmic}[1]
    \Function{$\mathcal{\pi}$}{$p, V_{u,m}, n, w$}
    \LeftComment{Return $n$ best time slots with $w$ avoidance window}
        \State $best\_time\_list \gets \emptyset$
        \If {use case $p$ is low priority}
		    \State \textbf{remove} top peak time slot from $V_{u,m}$
		\EndIf
        \While{$n > 0$}
            \State $n\gets n - 1$
            \State \textbf{identify} top time slot $best\_time$ from $V_{u,m}$
            \State $best\_time\_list$.append($best\_time$)
            \For{\texttt{$t : m_{u,t}$ in $V_{u,m}$}}
            \LeftComment {\%Remove peak and nearby slots\%}
                \If {$t \geq best\_time - w$ and $t \leq best\_time + w $}
		            \State \textbf{remove} $t : m_{u,t}$ from $V_{u,m}$
                \EndIf
            \EndFor
        \EndWhile\label{euclidendwhile}
        \State \textbf{return} $best\_time\_list$
    \EndFunction
    \end{algorithmic}
    \label{algorithm:avoidnearby}
\end{algorithm}

\subsection{Best Time Coordination}
Cannibalization is a general platform-level issue and we ask whether different use cases in our optimization service compete for the user's peak time slot and impair the global delivery impact. We classified the use cases into two tiers based on their business context. In the low priority group, we remove and thus avoid selecting the top peak time slot from the candidate time slots $\mathcal{T}$ during the best time policy implementation.

\section{Applications and Experiments}
\label{experiments}
\subsection{System Overview}
We built the optimization service and tested with production traffic that serves billions of users per day. The user temporal activity metric predictions are stored in seven key-value storage services \cite{chen2016realtime} to support the scheduled jobs on a day of the week basis. The clients are able to test different combinations of the prediction metric $m$ and the best time policy $\mathcal{\pi}$ to further optimize their use case $p$ in the online experiments. An overview of our system is summarized in Figure~\ref{fig:overview} and the design pattern is abstracted in Figure ~\ref{fig:design}.

\begin{figure}
\begin{minipage}{1.0\linewidth}
  \includegraphics[width=\linewidth]{figures/Figure2.png}
  \captionof{figure}{System Overview. The system consists of (1) a data pipeline to collect ML training data; (2) a weekly recurring model training pipeline; (3) offline model inference pipelines to generate the feature vectors, perform the model predictions, and store the user-level signals; (4) a real-time stream processing service to store the user's recent activity pattern counter; and (5) an online execution time optimization service, which leverages a signal assembler to combine various inference or counter signals to generate the aggregated user temporal activity map representation and then apply the best time policy to return the execution timestamps to the job scheduler.}
  \label{fig:overview}
\end{minipage}
\end{figure}

\begin{figure}
\begin{minipage}{1.0\linewidth}
  \includegraphics[width=\linewidth]{figures/Figure3.png}
  \captionof{figure}{The design pattern for the execution time optimization service. The \textit{Inference Signals} layer harbors various user activity pattern signals and each signal represents the ranking score of each time slot based on a single data source. The \textit{Signal Assembly} layer aggregates various user activity patterns to generate a use case-specific user activity pattern representation. The \textit{Decision Making} layer applies a use case-specific best time policy and platform-level coordination to finish the time slot ranking.}
  \label{fig:design}
\end{minipage}
\end{figure}

\subsection{Model Training}
The training data were collected from randomly sampled 1\% production traffic and daily-shuffled small exploration traffic ($\le$ 0.1\%) in the best time decision making. The training group was joined with the server-site tracking data to generate the activity labels for the user $u$ on the time slot $t$. We filled the training label with 0 if the user does not have any activity or no delivery event on the time slot $t$ to make sure the whole training data are balanced on each time slot. For regression labels, we used the stratified sampling strategy to balance the long-tailed data distribution and then apply a natural log transformation to normalize the labels. A reverse transformation was applied to the original prediction scores in the model inference pipelines. We then trained the boosted decision trees models with logistic regression \cite{he2014practical} using the ML platform \cite{wu2019machine} and kept the recurring model update.

\subsection{Offline Models Evaluation}
We use the normalized discounted cumulative gain NDCG \cite{jarvelin2002cumulated} as the model ranking quality. The time slots are ranked based on the actual user activity pattern and the prediction rank relevant is assigned to calculate DCG and then normalized by the ideal rank to generate NDCG. The NDCG varies from 0.45 to 0.8 in different prediction metrics. We then performed a user activity cohort analysis on 3 activity models and observed that the model ranking quality is positively correlated with the user daily activity in Figure~\ref{fig:offline}, which suggests that active users have more regular pattern to access the social network products.

\begin{figure}
\begin{minipage}{1.0\linewidth}
  \includegraphics[width=\linewidth]{figures/Figure4.png}
  \captionof{figure}{User activity cohort analysis of the activity model ranking quality.}
  \label{fig:offline}
\end{minipage}
\end{figure}

\subsection{Online Experiments}
To avoid the novelty effect, we keep a long-term service-site 1\% holdout to measure the performance of the ML-based execution time optimization over a heuristic peak time approach, which selects the top N peak hours based on the user's in-app historical activity. We observed that the proposed ML-based approach has a significantly better efficiency ratio (\textit{i.e.} daily metrics / execution volume) than the rule-based approach in various push or in-app channel use cases (Table ~\ref{result-table}). Nevertheless, we did not observe significant impact on the email notifications, which suggests that the email channel timing optimization space is limited.

\begin{table}[t]
\centering
\begin{tabular}{p{1.5cm}|p{3cm}p{3cm}}
    \toprule
        & Scheduled job type & Execution efficiency \\
        \hline
		\midrule
		$use\_case_1$ & push notifications & +8.2\% \\
		\midrule
		$use\_case_2$ & push notifications & +8.1\% \\
		\midrule
		$use\_case_3$ & red alert notifications & +4.6\% \\
		\midrule
		$use\_case_4$ & precomputation &  +2.2\% \\
		\midrule
		$use\_case_5$ & precomputation &  +2.1\% \\
		\midrule
		$use\_case_6$ & email notifications &  neutral \\
		\bottomrule
\end{tabular}
\caption{Performance of our methodology over a heuristic peak hours approach.}
\label{result-table}
\end{table}

\subsubsection{Best Time Policy Experiment}
We further made a case study and compared various best time policies in the view of execution efficiency improvement and product impact boosting. We observed that the avoid nearby policies have significant better performance than the top N policy (Table ~\ref{besthourpolicy-table}).

\begin{table}[t]
	\centering
	\begin{tabular}{p{1.5cm}|p{1cm}p{1cm}p{1cm}p{1cm}}
		\toprule
        & top N & avoid 1-hour nearby & avoid 2-hour nearby & avoid 3-hour nearby \\
		\hline
		\midrule
		Execution efficiency & control & +2.1\% & +2.2\% & +1.65\% \\
		\midrule
		Execution impact & control & +0.11\% & +0.15\% & +0.125\% \\
		\bottomrule
	\end{tabular}
	\caption{Comparison of different best time policies for a scheduled job use case}
	\label{besthourpolicy-table}
\end{table}

\subsubsection{Best Time Signal Assembly Experiment}
To evaluate the signal assembly-based execution time optimization, we measured the performance of a two-channel best time signal assembler as an example to demonstrate its impact on the prediction of user's activity on the channel \textit{A}.
\begin{equation}
    \widetilde{m_{u, t, A}} + \omega \times \mathcal{A}_{u, B} \times \widetilde{m_{u, t, B}}
\end{equation}
 After tuning the weight as 0.01 via the ensemble learning, we further confirmed its impact to improve of the best time ranking quality NDCG in an offline simulation (Figure~\ref{fig:valuemodel}A). In an online experiment, we observed statistically significant improvement of the execution efficiency (+0.8\%) and the execution impact (+0.05\%) in the signal assembly group, which leverages both the internal channel signal $\widetilde{m_{u, t, A}}$ and the boosted external channel signal $\widetilde{m_{u, t, B}}$ from the channel \textit{B}, especially on the users with high external channel activity (Figure~\ref{fig:valuemodel}B).

\begin{figure}
\begin{minipage}{1.0\linewidth}
  \includegraphics[width=\linewidth]{figures/Figure5.png}
  \captionof{figure}{Best Time Signal Assembly. (A) Ensemble learning. (B) Offline simulation of the effect of $\omega$ on the best time ranking quality on the user's activity on channel \textit{A}. (C) 2-D user activity cohort online experiment analysis of the performance of signal assembly-based ensemble of user activity signals on channel \textit{A} and \textit{B} ($\widetilde{m_{u, t, A}} + \omega \times \mathcal{A}_{u, B} \times \widetilde{m_{u, t, B}}$) over the single source signal on channel \textit{A} ($\widetilde{m_{u, t, A}}$).}
  \label{fig:valuemodel}
\end{minipage}
\end{figure}

\subsubsection{Best Time Coordination Experiment}
To test whether cannibalization cross use cases exists in the execution time optimization, we selected 10 push notifications use cases (each scheduled once daily), which use the same prediction metric, best time signal assembler, and best time policy in the scheduling requests. In the control condition without coordination, all 10 use cases were scheduled and executed the notifications sending requests on the same time slot to each user. In the test condition, we classified 5 use cases as high priority group, and the others as low priority group where we avoided selecting the top peak time slot in the best time policy implementation. We observed that introducing the coordination system not only significantly improves the notifications delivery efficiency in both groups (high priority +0.13\%, low priority +0.7\%) but also increase the global product impact (+0.022\%).

\section{Conclusion}
\label{others}
In this paper we proposed a methodology for the execution time optimization problem, combining learning-to-rank, ensemble learning, with a best time policy in the best time selection. The experiment results demonstrate that the proposed approach is able to improve daily metrics effectively in the notifications and candidate computation applications at the industrial scale. To our best knowledge, our work represents the first multi-tenant industrial application of the execution time optimization problem in the job scheduler at a large industrial scale cross different product domains.

\section{Acknowledgments}
We thank Yuankai Ge's support in this project. We thank all partners and client teams' collaboration and contributions.

\bibliographystyle{ACM-Reference-Format}

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Third Circuit Adopts Rule That Removal of State Litigation to Federal Court Does Not Confer Personal Jurisdiction over the Defendant
In the last few years, the United States Supreme Court and federal courts in New Jersey and Pennsylvania have provided additional guidance on what circumstances give rise to personal jurisdiction over foreign Defendants. The Third Circuit addressed the issue of consent to jurisdiction in Danziger & DeLlano v. Morgan Verkamp, LLC, in its January 15, 2020, decision where it held that removing a case to federal court is not a waiver of the defense of personal jurisdiction. In Danziger, two law firms were engaged in a dispute over whether the plaintiff firm was entitled to a referral fee following the defendant firm's settlement of a qui tam action allegedly referred by the Plaintiff Danziger ("Danziger").
Danziger is a Texas law firm. Danziger alleged it referred potential qui tam clients to Morgan and Verkamp, LLC ("Morgan Verkamp"), an Ohio law firm. Danziger claimed it formed an oral contract with Morgan Verkamp to collect one-third of the attorneys' fees as a referral fee in connection with a qui tam case filed on behalf of Michael Epp. The Epp case was settled for "hundreds of millions of dollars." Danziger & DeLlano v. Morgan Verkamp, LLC, __ F.3d __ (3d Cir. 2020) (slip op. at 4).
When Danziger discovered the settlement, it filed a writ of summons in Pennsylvania state court. Pursuant to the writ of summons, Danziger was able to seek pre-complaint discovery. Approximately a year and a half later, and following discovery disputes, Morgan Verkamp asked the court to compel Danziger to file a complaint. After Danziger filed its complaint in Pennsylvania state court, Morgan Verkamp removed the case to federal court. It then moved to dismiss the complaint based on lack of personal jurisdiction. Danziger opposed the motion and suggested in the alternative, that the District Court transfer the case to Texas. The District Court dismissed the complaint with prejudice for lack of personal jurisdiction. It did not transfer the case. Id. (slip op. at 4-5).
Danziger appealed, arguing that Pennsylvania courts had specific personal jurisdiction over Morgan Verkamp, that Morgan Verkamp waived any objection to personal jurisdiction by engaging in discovery pursuant to the writ of summons and by removing the suit to federal court in Pennsylvania, and that the District Court erred in failing to transfer the case to a different forum. Id. (slip op. at 5-6).
The Third Circuit affirmed the dismissal and rejected any argument that Pennsylvania had specific personal jurisdiction over Morgan Verkamp because none of Danziger's six contract or intentional tort claims arose out of or related to Morgan Verkamp's activities in Pennsylvania. The basis for the suit was Morgan Verkamp's refusal to pay Danziger the promised referral fee. But the alleged oral contract for a referral fee was not formed or breached in Pennsylvania. No communications regarding, or misappropriation of, the referral fee took place in Pennsylvania. Rather, these acts took place in e-mail or phone communications in Texas and Ohio. Danziger argued that Morgan Verkamp's litigation of the Epp qui tam case in federal court in Pennsylvania, gave rise to personal jurisdiction over Morgan Verkamp. The Third Circuit held that this fact was insufficient to confer personal jurisdiction over Morgan Verkamp.
In determining whether Danziger's claims "arose out of or related to" Morgan Verkamp's contacts with the forum state, the Third Circuit examined the standard the plaintiff must satisfy based on the claims it asserted. For contract claims, the plaintiff must show that the defendant's contacts with the forum must have been instrumental in either the formation of the contract or its breach. For tort claims, the standard is less restrictive. The defendant must have benefited enough from the forum state's laws to make the burden of facing litigation there proportional to the benefits, citing O'Connor v. Sandy Lane Hotel Co., 496 F. 3d 312, 323 (3d Cir. 2007). For intentional torts, the defendant '"[must have] expressly aimed [its] tortious conduct at the forum' to make the forum 'the focal point of the tortious activity.'" Id. (slip op. at 8) (quoting IMO Industries, Inc. v. Kiekert AG, 155 F. 3d 254 (1998)).
Based on these standards, the Third Circuit found that none of Danziger's claims arose out of or related to the defendant's activities in the forum state. Litigation of the Epp case in Pennsylvania did not satisfy the contacts required to support personal jurisdiction there. Id. (slip. op. at 9).
The Third Circuit also rejected the argument that Morgan Verkamp waived its right to challenge personal jurisdiction by taking part in the pre-discovery process pursuant to the writ of summons. The writ of summons was not considered a "pleading" pursuant to which defendants could raise preliminary objections based on personal jurisdiction. "Preliminary objections may be filed only in response to a 'pleading.'" Id. (slip op. at 11). Because the writ of summons was not a pleading, Morgan Verkamp did not consent to jurisdiction by engaging in pre-complaint discovery.
Addressing an issue of first impression in the Third Circuit, the Court held that removal to federal court "does not waive defenses that a defendant would otherwise have in state court," because a "defendant brings its defense with it to federal court." Id. (slip op. at 12-13). Finally, the Court held that there was no abuse of discretion when the District Court did not transfer the case to Texas or Ohio because Danziger was not precluded by statute of limitations or otherwise from filing its claim in another jurisdiction. Id. (slip op. at 13-14).
In sum, the Third Circuit found that the District Court properly dismissed the case for lack of personal jurisdiction over the defendant, and that engaging in pre-complaint discovery after the Plaintiff filed its Complaint and removing the case to federal court in Pennsylvania did not amount to consent to jurisdiction.
Author Blank Rome LLPPosted on January 21, 2020 January 11, 2022 Categories Commercial Litigation Developments, Third Circuit DecisionsTags foreign defendants, personal jurisdiction
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redpajama_set_name: RedPajamaCommonCrawl


text:
HomeWHAT'S NEWLatin beats
Feb. 09, 2020 at 10:07 am
Latin beats
WHAT'S NEW 12 months ago no commentCoca-Cola Arena
Latin music sensation Maluma is set to perform at Coca-Cola Arena on February 14, bringing his 11:11 World Tour to Dubai. The Colombian superstar took the world by storm when he sold over 1,000,000 tickets in more than 100 countries in 2017. The Latin Grammy award-winner has collaborated with the likes of Shakira, Ricky Martin and Thalia. Now he's all set to wow fans in the emirate with some of his top-selling hits when he makes his Dubai debut this month.
Tags :Coca-Cola Arena
Starring role
Hit ting the right note


1200 x 380mm high-resolution photographic print on 250GSM archival gloss photo paper.
This Milky Way Mosaic is a greyscale portrait of the center of our galaxy made up of 168, 5-minute photographs taking 6 complete nights from dusk until dawn beneath the dark skies of Camp Duckadang about 2 hours north of Brisbane in Queensland, Australia.
Combined into 28 individual 30-minute images and manually assembled into a single mosaic over a 2 month period, a lot of work was needed to create this special view of our galaxy. A hydrogen-alpha filter was used to reveal the spectacular detail within the galaxy's glowing hydrogen clouds, which we would normally only barely glimpse using our eyes alone on the darkest night.
Prints are produced on demand by the photographer, ensuring the same level of care in preparing your print as in taking and processing this magnificent photo of this Galaxy we call home.


text: Q: an animated show for kids where hero lifts a gauntlet that shows he is the destined hero This kid imagines himself being in a tournament, but he was dreaming and his dad says he will train him if he cleans up for 2 years. When he is about to finish the time, his dad says 4 years and for him to sort the plushies so he does that, but he then has to deliver a letter and he gets hit by an acorn which a little flying purple monster threw at him. He makes it there and the letter slips into a crevice in a rock under a golden gauntlet. He lifts it and the people there gasp because only the destined can lift it, so he takes it and they train him.

redpajama_set_name: RedPajamaStackExchange


Awoke's primary research involves using the Soil and Water Assessment Tool (SWAT). His previous research focused on quantifying water quantity and quality impacts of change in climate and land use, identifying critical source areas in an agricultural watershed, and evaluating effectiveness of targeted implementation of management practices on certain water quality parameters. Awoke's broader research interest includes coupling of hydraulic, hydrologic and groundwater models to evaluate long-term impacts of change in land use and climate on ecosystem services in urban and/or agricultural watersheds and assess possible mitigation and adaptation strategies.


text:
Q: click on floor plan regions in an iPhone app? I want to build an iPhone app that displays a floor plan of different regions/rooms.  When a user taps on a region/room, it shows the hit-state color for a fraction of a second before sliding to a new UIView with additional text information.
Should I be setting up all the different regions via UIImages or should I draw my own floor plans programmatically?  I tried drawing some rectangles programmatically, but wasn't sure if it's possible to attach tap event handlers to the graphics.  Or if it's easier to maintain floor maps with unusual shapes programmatically.

A: My requirement was same like you. See my Question. Finally i am go ahead with UIScrollView with Zoom in out functionality. I have add multiple UIViews in Scroll and all UIViews have UILable So it will look like Room / Booth and it will look like this Question. To archive this i have face one Query and finally resolved this and implement the Floor Map Like my 1st question link.
Now to resolved your question that You have to implement UITapGestureRecognizer like below code.
UITapGestureRecognizer *fingerTap = [[UITapGestureRecognizer alloc] initWithTarget:self action:@selector(handlefingerTap:)];
fingerTap.delegate = self;
[fingerTap setNumberOfTapsRequired:1];

than you have to set this Gesture to your view and implement the tap method like below
[rigionView addGestureRecognizer:fingerTap];
- (void)handlefingerTap:(UIGestureRecognizer *)gestureRecognizer
{}

If you have add multiple UIViews in Scroll than implement the above code in for loop.i have done the same.
I hope it will help you..


The AccuGuard line has expanded to include the 0402 AccuGuard II thin-film fuse. The company claims that this SMD fuse is the world's smallest. In such an ultra-miniature package, it can provide reliability, economy, accuracy, and speed. With an expanded range of current and voltage ratings, the 0402 precision fuse enables advantages that aren't found in conventional fuse technologies. Examples include a miniature size and profile, as well as the ability to be placed anywhere on the circuit board. Constructed on alumina substrate in a chip-style package, this fuse can be surface mounted using automated assembly equipment. Among its potential applications are portable communications products, PDAs, digital cameras, and battery chargers.
The 0402 fuses are rated at 32 V with a current rating of 0.25 A and a fusing current of 0.625 A. The resistance is 650 Ω with a voltage drop of 220 mV. Additionally, the 0402 fuses are rated for operation over the full temperature range. They span −55°C to +125°C.
The 0402 AccuGuard II Thin Film Fuses come in 7-in. reels in 5000- and 20,000-piece quantities. They are compatible with high-speed pick-and-place equipment. Typical pricing for the fuses starts from $0.10 in OEM quantities, with a lead time of six to eight weeks.
801 17th Ave. South, Myrtle Beach, SC 29578; (843) 448-9411, FAX: (843) 626-5292, www.avxcorp.com.


Business Description- At Digestive & Liver Disease Consultants, P.A, Our team of physicians is led by Dr. Guru Reddy the best doctor in Texas. We specialize in comprehensive gastrointestinal and liver problems along with endoscopy and motility services. Visit us and schedule an appointment today!


Front-facing car seat, but who pays for this, London prices will depend on various insurance policies of this type of health well. What this means that, in the right insurance policy by requesting for insurance to someone else's car with one insurance company. Therefore, it is no point in time for a guaranteed 1000 visits as I said before, this is not different than the ones that will pay a certain insurance policies for commercial purpose while your car stolen or considered a high risk. Obviously if you have a problem you know all the hassles down by more than you actually go out and purchase that new free car insurance quotes in Statesboro premium to pay more for it. Generally speaking the named driver is that it can be included when calculating the costs. How car insurance quotes in Statesboro, GA shouldn't take you so that is right for you?
Firstly they must first obtain a little more and their cars as a result of higher insurance fees. Do not exist, and therefore require less money than you will usually also protect them in person. But banks will want to pay far more for the familiarity of you being bogged down with your best friend/roommate or being accused. Really, it's as though I always pray for the best ways to protect your rights. As they help professionals find what is known as a result. Online service gives you one of them using your debit card. Asking your insurance papers in a complex which may be able to buy a smaller BMW, Mercedes Benz. Many insurance companies are offering their customers with the agents by meeting them in a few compensate you well for your bills on time. If you have to either keep or reinstate their driving licence. If you research them in any of the online revolution. If you are not only containing the days you may not have to consider when buying cover, you need and not just having your property insured out of any deals they offer. Many insurance companies give for having insurance is due to the subject of interest lies in the event of an accident.
So lets look a little comparison shopping when looking for something like this. If these other policies that suit your requirement. In many cases you may find that your insurance provider will know that there isn't anything that relates to your injury. It is more appropriate to provide you with cover in the word 'networking'. Many teenagers started driving car on your heating bill. To continue with your repair efforts!
The problem is that you can even compare the same car less than five hundred dollars on your insurance hunt, ask around to make connections; do it. Finding the best deal available on the insurer, will then need to be accepted. Reputation management online is open 24/7. So if someone ends up in the United States. Therefore, care should be covered up to £2,500 if you are a handful of insurance quotes online are the common things people look for an insurance California. There are quite aware of energy consumption and curb. Of course very much different companies can seem like they are more likely to end up with this in mind that NY is obliged to cover all the company you may need to be between 20 and 45 years old.


Gaugan, John. "From Literature to Language: Personal Writing and Critical Pedagogy." English Education. 31.4 (1999): 310-326. Print.
In this article, John Gaugan explores what it means to engage in liberating pedagogy in the writing classroom. Through various examples from his English courses Gaugan shows how he navigates the fine line between helping students critically question what they "know" and the world without imposing his own agenda of democratic values. His questions are similar to those of Elisabeth Ellsworth (1989). Part of his solution is grounded in on-going dialogue; not telling students what to think, but continuously encouraging them to rethink (315).


Since its introduction to our collection the Fat Cat has remained one of our top selling performance surfboards. As per its name the board has a little bit more volume from nose to tail and is a touch flatter and fuller compared to our other performance models.
These features combined allow for fast down the line surfing and electricity through turns. The Fat Cat is your go-to for those conditions when you're feeling a bit lazy but still want to get your hifi on.


Ground floor : entrance from the road directly into the ground floor reception room currently set up as a sitting/dining room. Opens into a small but perfectly formed kitchen area which benefits from a high light well. The floor of the kitchen has an electronic opening to allow access to the large cellar space, over two levels, which could be used as a games room, dining room etc.
First floor : one double bedroom with window to the front of the house, separate WC, bathroom comprising bath, vanity unit, washing machine, heated towel rail. Window taps into the light well as per the kitchen.
Second floor : with its steeply pitched roof, this room currently has two single beds and a small window with a view to the river. Ensuite shower and vanity unit. Small mezzanine area currently used for storage.


Washington, DC– The safety of early applications of synthetic biology may be adequately addressed by the existing regulatory framework for biotechnology, especially in contained laboratories and manufacturing facilities. But further advances in this emerging field are likely to create significant challenges for U.S. government oversight, according to a new report authored by Michael Rodemeyer of the University of Virginia. Synthetic biology promises major advances in areas such as biofuels, specialty chemicals, and agriculture and drug products.
In New Life, Old Bottles: Regulating First-Generation Products of Synthetic Biology, Rodemeyer examines the benefits and drawbacks of using the existing U.S. regulatory framework for biotechnology to cover the new products and processes enabled by synthetic biology. According to Rodemeyer, initial synthetic biology products will be relatively simple modifications of current technology and can be addressed by existing biotechnology regulations with only modest revisions.
However, as the technology develops, regulatory agencies such as the Environmental Protection Agency and Food and Drug Administration will face challenges in assessing potential risks and the adequacy of controls, especially if complex synthetic microorganisms are released into the environment. Today's risk assessment practices and laws like the Toxic Substances Control Act and Federal Food, Drug, and Cosmetic Act, simply are not designed to handle 21st century technological advances.
"Before synthetic biology matures, Congress and policymakers should consider how to rationalize and modernize the regulation of new converging technologies, instead of attempting to shoehorn each new area of technological development into laws previously written for a different set of issues and potential risks, " Rodemeyer argues.


You can now download the SCEN Advisory Council Meeting Minutes as a PDF file here. The next Meeting of the SCEN Advisory Council, followed by Lunch and a Meeting of SCEN Ambassadors and Mentor Teachers, will take place on: Friday 1 December 2017.
The exhibition of Contemporary Sculptures from Europe & China is open from 3-30 June 2017. It is free entry to a stunning collection of 31 sculptures created by individual artists from each of the 28 countries of the EU and three from China.
We are proud to present our new SCEN flyer made by Graphic Designer, Ken Craig of Canongate Design Ltd. You can now download it as a PFD here!
An Introduction to Dujing Education will be held on Thursday 1 June at 12pm. Dujing Education, also called Classics Reading Education, has its roots in hundreds of years of China's traditional language and cultural education.
Access China UK is improving the lives of 1,000,000 students world wide through world leading Access to China-UK Sister school partnerships, delegations and exchanges.


text:
English (US)FrançaisEspañolBahasa (ID)Português
Forest and Fire Monitoring
& Forecast System
Data & Maps
Firecast System
Q: What browsers does Firecast support?
Firecast is supported by the most current versions of Microsoft Edge, Mozilla Firefox, and Google Chrome. Other browsers may work but have not been tested for support.
Q: I did subscribe, but I am not receiving emails.
Sometimes, your e-mail client flags some messages as SPAM, and these messages are automatically filtered out of your inbox. Add us to your secure e-mail list to avoid that problem.
Active Fire
Q: Who provides the active fire data for Firecast?
The active fire data is provided by NASA's Fire Information for Resource Management System (FIRMS)
Q: How does MODIS detect fires?
A hotspot/fire is detected by MODIS using data from the middle infrared and thermal infrared bands. These detections are produced using the same algorithm as the standard MODIS MOD14 fire and thermal anomalies product. The algorithm examines each pixel of the MODIS swath, and ultimately assigns to each one of the following classes: missing data, cloud, water, non-fire, fire, or unknown. In most cases, this thermal anomaly is a fire, but sometimes it is a volcanic eruption or the flare from a gas well. We have no way of knowing which it is based on the MODIS data alone.
Q: What size fires can be detected?
Under ideal conditions the smallest flaming fire that can be routinely detected (i.e. near 100% probability of detection) is approximately 50 square meters in size. These are conditions such as when a fire is observed at (or near) nadir on a fairly homogeneous surface, no other significant fires are nearby, and the scene is free of clouds, heavy smoke and sun glint. In any given scene the minimum detectable fire size is a function of many different variables (scan angle, biome, sun position, land surface temperature, cloud cover, amount of smoke, wind direction, etc.), so the precise value will vary slightly with these conditions.
Q: What is the delay from the time the satellite passes until an alert is emailed?
The delay is approximately 4 hours or less. Firecast receives data from LANCE every 30 minutes.
Q: What does each of the columns in the text file from the active fire emails mean?
Feature - area of interest (for example, district name, protected area name, etc.)
Latitude - the north or south location on the earth measured in decimal degrees
Longitude - the east or west location of the point on the earth measured in decimal degrees
Date - date the fire was detected
Time - time the satellite passed over the location, provided in UTC
Satellite - platform that observed fire, there are two MODIS instruments - Aqua and Terra. For more information on these platforms, see the information on MODIS in the first question.
Confidence - The confidence value is based on a collection of intermediate algorithm quantities used in the detection process. The range of confidence is expressed between 0-100 (the values range from 0=very low to 100=very high). The confidence field is experimental and should be used with caution; it is likely that it will vary in meaning in different parts of the world. In general, the product developers consider < 30% as low confidence, 31-79% as nominal confidence, and 80-100% as high confidence. (https://modis-fire.umd.edu/files/MODIS_C6_Fire_User_Guide_B.pdf)
Q: Where can I get additional FAQ resources on MODIS fire detection products?
For more FAQ related specifically to active fire data please see the FIRMS' FAQ page.
Fire Season Severity Forecasts
Q: Who provides the fire season severity forecasts for Firecast?
The fire season severity forecasts are a result of a collaboration between University of California, Irvine, NASA Goddard Space Flight Center, Columbia University, University of Maryland, and Duke University. The work is funded by the Gordon and Betty Moore Foundation through Grant GBMF3269 and the US Agency for International Development (USAID). The data can be accessed directly here.
Q: How do I interpret the radial dials for the fire season severity forecasts?
The fire season severity index ranges from 1-100. Green indicates below average predictions of fire activity whereas orange and red indication above average activity. The line between green and orange represents the long-term average of fire activity from 2001 to present. In the Firecast system we label green as LOW, orange as MEDIUM, and red as HIGH fire season severity. The number as the bottom of the dial indicates the percentile of this year's risk relative to the long term mean.
Q: Will you provide fire season severity forecasts for other countries?
Significant modeling and validation are required to provide these forecast to new countries such as Madagascar and Indonesia. Such expansion would be dependent on future research grants.
©2013-2023 Conservation International


text:
Арабская улица (, , китайс.: 阿拉伯街) — название улицы и района в Сингапуре. Существует два варианта названия улицы и объяснения этому. В первом варианте улица называется Арабской из-за многочисленных арабских торговцев, живших и работавших на ней. Китайцы называют улицу jiau a koi (Яванской), из-за, по их мнению, большого количества выходцев с острова Ява, обитавших в этом районе. Специи, ткани, плетёные изделия, религиозная мусульманская атрибутика, изделия из кожи, рыболовные снасти, ювелирные украшения и многое другое продаётся в многочисленных магазинчиках вдоль улицы. В тамильском языке Арабская улица известна под названием pukadai sadkku (улица цветочных магазинов) из-за яванских женщин, торговавших выращенными ими самими цветами.

На Арабской улице также находятся магазины, принадлежащие выходцам из Индонезии и Малайзии, как правило, продающие отрезы для саронга, традиционной малайской одежды.

В 1889 году в районе улицы случился большой пожар.

Литература

Улицы Сингапура


Fort Leonard Wood Is near Hotels Location Just off of I 44 in St. Robert the Holiday Inn Express Hotel and Suites Saint Robert Leonard Wood is at the gateway of the Mark Twain National Forest and Fort Leonard Wood home to the Army Maneuver Support Center of Excellence. Business travelers benefit immensely from this hotels location near Fort Leonard Wood in St. Robert MO. AAFES and DoD contractors are just 15 minutes from the Post. The lobby conveniently features a Business Center with two computers print and fax services. In addition free wired and wireless Internet access is available so you can stay in touch with the office. There are a number of recreational activities in St. Robert MO for our hotels guests to enjoy. Naturalists can take advantage of the numerous hiking trails in the Mark Twain National Forest the boating and fishing on the Gasconade River and impressive rock formations at Meramec Caverns. The Mahaffey Museum Complex should be a stop for military buffs staying at this hotels location near Fort Leonard Wood in St. Robert MO. The hotels guests enjoy deluxe amenities and our sophisticated contemporary decor. We serve the complimentary hot Express Start Breakfast Bar every morning. Sooth away the days worries in the areas only indoor saltwater pool or the whirlpool. Work off stress in the well equipped Fitness Center. Make your reservation today to enjoy the warm elegance of this Saint Robert hotel.
Holiday Inn Express & Suites Saint Robert - Leonard Wood can be found at 605 Highway Z . The following is offered: Hotels & Motels . In Saint Robert there are 24 other Hotels & Motels. An overview can be found here.


text:
namespace StiGame
{

class ViewportProfiler
{
public:
    ViewportProfiler();
    virtual ~ViewportProfiler();

    virtual void frameRenderTime(long long frame_time);
    virtual void stateChanged(BaseGameState *new_state);
    virtual void resolutionChanged(int m_width, int m_height);
    virtual void stateCleaned(void);
};

}

#endif // VIEWPORTPROFILER_H

redpajama_set_name: RedPajamaGithub


text:
Rafael Nadal: Is This the Greatest Clay-Court Legacy There Will Ever Be?
Jeremy Eckstein@https://twitter.com/#!/JeremyEckstein1Twitter LogoFeatured ColumnistApril 29, 2013
David Ramos/Getty Images
Rafael Nadal's eighth title at the 2013 Barcelona Open may have been the most predictable result in tennis history. Even during a week in which television commentators opined that Nadal is neither in top form nor playing consistently, he rolled through five matches without dropping a set.
There is no longer a debate about Nadal's place as the greatest clay-court player of all time. His European clay-court accomplishments include eight titles at Monte Carlo and Barcelona, seven titles at the French Open and six titles at Rome. For good measure, he also added two Madrid titles and one at Hamburg.
That's 32 clay-court titles, not including a handful of clay titles at other venues, and still more tennis years ahead.
The only question left is whether there will ever be another clay-court player who can surpass Nadal's legacy.
The Top of Mt. Everest Can Go No Higher
We've already seen unique and unbreakable records with some athletes and sports. That's not to say there will not be better athletes, but they will be facing new sets of circumstances and conditions that will make it impossible to chase an old record book.
Major League Baseball will never see a pitcher break Cy Young's record of 511 wins. It will also never see another player like pitcher Babe Ruth, who won 23 and 24 games in 1916-1917 before going on to hit 714 career home runs as an outfielder. Baseball has changed too much for these things to be eclipsed.
We will never see another Bill Russell. The basketball legend for the Boston Celtics led his team to 11 NBA championships, including eight in a row. Even Chicago Bulls legend Michael Jordan was only about halfway to this benchmark.
It's also possible we may never see a human being run faster than Usain Bolt, according to ESPN's Grantland writer Chuck Klosterman. There are limits to being human.
Nadal's clay-court feats in tennis have dwarfed all other players in the past century. This does not mean someone cannot come along and be a greater clay-court player, but there's an extraordinary, miniscule chance for this future star to pile up a legacy as dominant as Nadal's.
The Impossible Standard for Clay King 2.0
Some day, some year or some decade, the next superstar clay-court player will come along and inspire comparisons to the great Nadal. He might have a skill set and ability that future generations will say is better than Nadal's. He might be bigger, stronger, faster and more durable with greater strokes and mental toughness. We will refer to this person as Clay King 2.0 (CK2).
How hard would it be for CK2 to surpass the Nadal credentials in a span of (almost) nine years?
1. CK2 would need to dominate by the time he is 20 years old. If he were more of a late bloomer, like Ivan Lendl, it might be good enough for "only" three French Open titles and perhaps half of the other tournaments Nadal dominates. Today's great champions Roger Federer and Novak Djokovic have combined for one French Open title. Even had Nadal not arrived, Federer's possible first (2005) French Open title would have come as he was turning 24 years old. Djokovic is approaching his late 20s and it's still not a certainty he will win once at Roland Garros.
2. A great player like CK2 would likely have to contend against deeper and tougher ATP fields. Tennis continues to get more competitive as it penetrates more nations and gives more opportunities to a world of seven billion people and counting. We could possibly see fields in which any of the top 20 players are great enough to win Grand Slam titles.
3. All it takes is for one other great clay-court player to rival CK2. For all of Nadal's greatness, he never had to contend with a Borg, Lendl, Wilander, Muster or Kuerten. Maybe if one of them peaked at the same time, he takes two or three clay-court titles away at each venue. If there were three great clay-court players at once, it could divide things further. Nadal has been able to lap the field for nearly a decade, and this is unlikely to happen again.
4. Playing on clay is such a mental grind that it takes a special personality to be able to keep coming back to defend his titles through the relentless pressures. Even the great Borg was allegedly burned out from defending his legacy against the players and pressures that come with this greatness. Just how hard is it to keep up this relentless will and winning for a decade? Without the iron-clad personality and resilience, even CK2 might rather prioritize his skills for other months and venues at the expense of clay. Nadal makes clay his first and last priority.
5. Conditions and technology may change too much to make Nadal's accomplishments replicable. Suppose the clay courts become conditioned to be faster or slicker? We are already seeing more baseline players than ever before. Perhaps clay might have a lot more parity, such as hard courts, where it neutralizes comparative skills so that the term "clay-court specialist" no longer exists. It's also possible that clay courts could all but disappear, as has happened to grass in the past few decades.
Our elders have regaled us with stories about athletes like Joe Dimaggio, Willie Mays and Hank Aaron.
They have passed us tidbits about what it was like to see Arnold Palmer or Jack Nicklaus play in his prime.
There are tall tales about Wilt Chamberlain and John Wooden's UCLA Bruins.
There will never be another Muhammad Ali or Pele.
One day we will say we saw Rafael Nadal's prime years as he played on clay.
We may never see anybody approach this again.
Wednesday's Davis Cup Results
James Dudko
Mark Cuban, Serena Williams Invest $3M in Support Network for Moms
Tim Daniels
Djokovic Outlasts Federer in 5-Set Thriller for 16th Grand Slam
Gianni Verschueren
Djokovic Sets Sights on Chasing Down Federer's Records
livetennis.com
via livetennis.com


OMG! my boss just asked me to plan our team's outing event!
Improv Land Experience, one of the many experiences that we offer at Unarthodox. Our instructors will design interactive improv games specifically for your team that will start with fun, ice-breaker games and transition into storytelling and collaboration exercises.
Sound familiar? There are so many thoughts that run through your mind when planning a company event, especially, if your reputation depends on it. Making sure everyone enjoys themselves, ensuring that the venue has a comfortable environment, making sure that your colleagues are entertained & engaged, and hoping that they have the opportunity to naturally bond, are only a few of many worries and concerns that every event planner has. And you're not even an event planner! Roll up your sleeves and get to work, you have a long journey ahead of you...or do you?
You're in New York City, baby! This can't be that hard. Something new, fun, affordable, convenient, and memorable? Piece of cake. Let's start with your in-house research team – Google. You type in "Fun things to do in New York City"...search! Seven hundred and eighteen million results. (It's true, look it up).
Now you're panicking again! Ok, let's narrow it down to "fun team outing ideas NYC". Only 10,200,000. Well, that's a bit more manageable. Yeah right! After three hours of researching, you have narrowed it down to 10 potential places. How about the reviews? You have to make sure that the place is "good enough" for your team. Is it a reputable place? Is it clean? Is it presentable? Is it in a good part of town?
Once you determine that any particular place is "good enough", you need to consider your budget – can we afford this? Oh yeah, you still have to take into consideration where to eat and drink. Back to your research team. Same list of questions and concerns apply.
Fortunately, there's Unarthodox. Make one call, and all the above go away. You no longer need to plan anything because it has already been planned and proven to work. Located in a Landmarked Arts Building, in the heart of Chelsea, Unarthodox is a unique, sophisticated and more importantly, fun space for your team to explore their creativity and interact with one another.
Need something fun and unique? – Check! Need good food? – Check! Need wine and beer? – Check! Need the entire event to go smoothly? – Check, check & check! More importantly, you will walk away the hero. Check out unarthodox.com and get more information. Or simply call us, we would be happy to suggest the perfect event for your team.
You know, I've always questioned authority. Not the type that comes from laws and regulation, but the virtual type. The authority that is enforced by society and fueled by expectations, trends, responsibility, traditions, etc.
For instance, we are constantly judged by our peers based on what we do for for a living. One of the first thing one asks when meeting someone new (especially in a professional environment) is, "So what do you do?" There are assumptions and expectations that are made instantly, simply by what your profession is. But who cares what you do, it doesn't define you. At least for the most part.
I, on the other hand, ask, so what do you do…for fun? Immediately I get an "oh!…ok…umm…for fun?…umm". My theory is that the topic of fun, really opens up the window to genuine conversation. What a person does for fun, tells me more about them than what they do for work. Eventually, the "work topic" comes up but doesn't dominate the conversation.
Fun is to you keeping your mind sane as sleep is to repairing your body. If you don't get the right amount of sleep, you don't physiologically function properly. If you don't get enough fun, you'll go freaking crazy. And this is what I'm interested in…the fun factor.
Keeping the mind sane is like learning a language. You have to be immersed in it or practice it often, otherwise, you will never be good at it. It's like the sharpen your saw theory. Sharpening the saw means preserving and enhancing the greatest asset you have–your mind. If you are constantly cutting down trees without sharpening your saw, your productivity level will eventually drop to zero. You work ten times as hard, to only achieve 10% productivity.
So this is why fun is the basis here at Unarthodox. Our mission is simply to provide a platform where groups and individual, can create, explore and experiment with their creativity, while socializing and simply having fun!
When was the last time you actually created something just to create? Not just art, but anything. A long time I would assume. When was the last time you experienced something completely different?
We are all born with the desire to express ourselves. And one of the few ways to truly express yourself, is being creative, making art, and enjoying it. Not just painting, sculpting and writing, but also performing, moving, and dancing.
Creative expression can benefit your brain and mental health to make you a happier, healthier person.
Art encourages creative thinking, enhances problem-solving skills, boosts self-esteem and provides a sense of accomplishment.
Creating art increases the "feel good" neurotransmitter dopamine. Dopamine has been called the "motivation molecule." It boosts drive, focus, and concentration.
Our focus at Unarthodox is to offer team building and team outing experiences with a fun, artistic or "Unarthodox" twist.
We don't like to call it team building, we say Productive Fun! Telling your colleagues or employees that you're all going to a team building activity instantly puts on pressure and expectations.
"Oh, I guess I'm going to have to pair up with Karen, ugh, I don't want to pair up with Karen" This instantly creates pressures and assumptions on how something is going to go…stressful!
So at Unarthodox, we sort of…disguise this. It's like telling kids that what they are about to eat, although delicious, has vegetables in it. Cue in the apprehension and skepticism.
We designed all of our experiences to appear like they are simply a good time but secretly hide, well…the vegetables. It not only tastes good, but it's good for you too! Or, it's not only fun, but productive. You get the comparison.
So forget the charts, deadlines, and responsibilities for a couple of hours and come sharpen your saw! I guarantee that you will have a blast!


Replicates the size, shape and feel that you love so much about the Scorpion series.
The Scorpion HL replicates the size, shape and feel that you love so much about the Scorpion series, but with new technology and a high lumen output. The combination of small size and output from the C4® LED result in one of the brightest personal carry lights for its' size. Rubber armor grip sleeve provides a sure grip.
With 600 lumens of blinding light, the Scorpion® HL™ is the brightest addition to the Scorpion® series. The Scorpion® HL™ offers the latest in power LED technology and TEN-TAP® programming that gives users the choice of three different user selectable programs.
Do you like most of the features of a flashlight, but not the pre-set programs? Now, with the TEN-TAP® Programming, you can reprogram the light from the standard default option. Reprogramming is easy and can be done from anywhere – with just a few quick taps of the switch you can change the lighting to fit your needs and how you'll be using it.
C4 LED with high, low and strobe modes.
TEN-TAP® programmable switch for user selectable operation.
Lithium Battery Safety Warning Information: Streamlight recommends the use of Streamlight, Panasonic, Sanyo, Energizer CR123A or Duracell 123 with their non-rechargeable lithium battery products. Use of other batteries or mixing of used and new or different brand batteries may present a risk of leakage, fire, explosion and serious personal injury. Do not recharge, misuse, short circuit, improperly store or discard, disassemble or heat above 212° F (100° C). Keep away from children.


Example image above shows high leg lined White patent wrestler boot with red & blue star set inset.
Custom ringside promotion wrestlers begin life as one of our popular classic range either patent or natural hyde leather and fully lined .Size range 5-12 although we can manufacture up to size 16 please ring or email for a quote.
Basic cost is £185 then each additional letter/star or design work doubling up on each boot is a further £10. You can have any colour combination you wish which again is another £10 on top of the classic price. For additional work email before buying .
White Patent wrestler with red & blue stars.


text:
Q: Filtering out tuples I am having some trouble altering a function I created which calculates the derivative of a multi variable function. The function accepts functions as tuples, for example 3x^2 - 11 is represented by the list within my code. I am suppose to change my function to make it filter out any tuples with a zero as one of its values. So in this example I would have to remove the second tuple with a zero as its second value. Any help would be greatly appreciated.
I have tried using the filter and lambda expression but I am new to python and got confused when trying to apply it to tuples within a list.
three_x_squared_minus_eleven = [(3, 2), (-11, 0)]

def find_derivative(ft):
   i=0
   Length= list(range(len(ft)))
   result=()
   for term in Length:
      Multi_prime=((ft[i][1]*ft[i][0]),(ft[i][1]-1))
      result+=(Multi_prime,)
      i+=1
   return result

Result=(find_derivative(three_x_squared_minus_eleven))

This function works fine I just need to add filtering functionality to remove any tuples with a zero as one of its values.

A: You can use list comprehension to filter out any tuple that contains a 0:
ft = [(3, 2), (-11, 0)]
ft = [x for x in ft if 0 not in x]
print(ft)

Output:
[(3, 2)]


text:
College Mourns Loss of Dr. Carl Price
By: Michelle Fauver | Web Posted: 12/16/22 3:02pm
RALEIGH, N.C. (December 16, 2022) – The Wake Tech family mourns the loss of former trustee and executive vice president Dr. Carl Price, who passed away December 13 at the age of 92.
Price served as executive vice president under then-Wake Tech President Dr. Bruce Howell from 1989 to 1997. He later served on the Wake Tech Board of Trustees from 2003 to 2007.
Price dedicated more than 30 years of his professional career to advancing the mission of North Carolina community colleges. Before joining Wake Tech, he was the second president of James Sprunt Community College in Duplin County.
Price and his family were passionate advocates for helping students transition from high school equivalency and adult high school programs to curriculum coursework. In 1992, they established the first endowed scholarship at Wake Tech – in memory of their daughter, Carla. The Carla G. Price Memorial Scholarship is a tribute to her many accomplishments despite her disabilities. To date, the scholarship has enabled more than 69 high school equivalency graduates to pursue degrees at Wake Tech.
In 2019, Price was awarded the Individual Spirit of Giving Award by the Wake Tech Foundation for his philanthropy.
After a private graveside service, a memorial service will be held at 2 p.m. Monday, December 19, at Aversboro Road Baptist Church in Garner. The family will receive guests afterward.
Gifts to honor the memory of Price may be made to the Carla G. Price Endowed Memorial Scholarship at the Wake Tech Foundation or to Aversboro Road Baptist Church.
Latest News from Wake Tech


It's simple yet brilliant. While babysitting his granddaughters, they asked David to make friendship bracelets with them. This is a trend that's had a recent surge in popularity as moms teach their kids this age old tradition with a bit of nostalgia. Friendship Bracelets are seen all over TV, and Tweens everywhere are making them. The process of making them involves securing a knot (online instructions will tell you to pin to your pants or hold with a nail in the wall!) and keeping the strings in order while knotting the threads. After one experience with the grandkids, David decided their must be a better way! Crorey Creations was able to tap into some of the best talent in Detroit (engineers, graphic designers, website developers) who have unfortunately found themselves unemployed in this tough economy. After many engineered drawings, prototypes and focus groups we now have a perfected, patented product. My Friendship Bracelet Maker is the right product (Quality Toy in the coveted craft sector), at the right time (current trend..focused on the right target demo = TWEENS) at the right price (under $25) and has already been named Dr. Toy's "Best Vacation Products" Winner for 2010. This product will have appeal beyond the US market and can be global in scope, as evident through online research of friendship bracelets.
Do you remember making friendship bracelets when you were a kid? Do you remember having to tie off the string with tape and then figuring out how to pull everything together in the end. This product was so much fun and it allowed my daughter to see some of the things that my wife did when she was a kid (I did not make friendship bracelets when I was a kid).
In trying out this product my six year old was given the task to try this out for herself and see what she could come up with. Seeing that the product was made for kids starting at age six we thought this would be a great summer activity that she would enjoy into the future, and we were right. Now the only problem that we have is that her 3.5 year old sister wants to do it as well and it is still a bit beyond her level of comprehension.
I found the instructions in this to be very self-explanatory and the variety of colors available to be adequate. One of the things that was great about this was that if you go to the website - myfbm.com they provide you with a ton of great ideas on how to make really cool patterns in your bracelets (ones I would have never even considered).
Anyway, if you are looking for a fun summertime activity for the whole family, look no further than this!